干旱区生态环境知识资源中心(Arid): Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques

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DOI	10.1007/s10661-024-12294-x
	Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques
	Mosaid, Hassan; Barakat, Ahmed; John, Kingsley; Faouzi, Elhousna; Bustillo, Vincent; El Garnaoui, Mohamed; Heung, Brandon
通讯作者	Mosaid, H
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2024
卷号	196 期号:2
英文摘要	Soil serves as a reservoir for organic carbon stock, which indicates soil quality and fertility within the terrestrial ecosystem. Therefore, it is crucial to comprehend the spatial distribution of soil organic carbon stock (SOCS) and the factors influencing it to achieve sustainable practices and ensure soil health. Thus, the present study aimed to apply four machine learning (ML) models, namely, random forest (RF), k-nearest neighbors (kNN), support vector machine (SVM), and Cubist model tree (Cubist), to improve the prediction of SOCS in the Srou catchment located in the Upper Oum Er-Rbia watershed, Morocco. From an inventory of 120 sample points, 80% were used for training the model, with the remaining 20% set aside for model testing. Boruta's algorithm and the multicollinearity test identified only nine (9) factors as the controlling factors selected as input data for predicting SOCS. As a result, spatial distribution maps for SOCS were generated for all models, then compared, and further validated using statistical metrics. Among the models tested, the RF model exhibited the best performance (R2 = 0.76, RMSE = 0.52 Mg C/ha, NRMSE = 0.13, and MAE = 0.34 Mg C/ha), followed closely by the SVM model (R2 = 0.68, RMSE = 0.59 Mg C/ha, NRMSE = 0.15, and MAE = 0.34 Mg C/ha) and Cubist model (R2 = 0.64, RMSE = 0.63 Mg C/ha, NRMSE = 0.16, and MAE = 0.43 Mg C/ha), while the kNN model had the lowest performance (R2 = 0.31, RMSE = 0.94 Mg C/ha, NRMSE = 0.24, and MAE = 0.63 Mg C/ha). However, bulk density, pH, electrical conductivity, and calcium carbonate were the most important factors for spatially predicting SOCS in this semi-arid region. Hence, the methodology used in this study, which relies on ML algorithms, holds the potential for modeling and mapping SOCS and soil properties in comparable contexts elsewhere.
英文关键词	Carbon sequestration Digital soil mapping Predictive modeling Soil erosion Srou River
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001142558200006
WOS关键词	REMOTE-SENSING DATA ; ORGANIC-CARBON ; FEATURE-SELECTION ; HEAVY-METAL ; LAND ; SUSCEPTIBILITY ; SUITABILITY ; DEPOSITION ; MODELS ; MATTER
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403589
推荐引用方式 GB/T 7714	Mosaid, Hassan,Barakat, Ahmed,John, Kingsley,et al. Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques[J],2024,196(2).
APA	Mosaid, Hassan.,Barakat, Ahmed.,John, Kingsley.,Faouzi, Elhousna.,Bustillo, Vincent.,...&Heung, Brandon.(2024).Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques.ENVIRONMENTAL MONITORING AND ASSESSMENT,196(2).
MLA	Mosaid, Hassan,et al."Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques".ENVIRONMENTAL MONITORING AND ASSESSMENT 196.2(2024).